twinstim: Fit a Two-Component Spatio-Temporal Point Process Model

In surveillance: Temporal and Spatio-Temporal Modeling and Monitoring of Epidemic Phenomena

Fit a Two-Component Spatio-Temporal Point Process Model

Description

A twinstim model as described in Meyer et al. (2012) is fitted to marked spatio-temporal point process data. This constitutes a regression approach for conditional intensity function modelling. The implementation is illustrated in Meyer et al. (2017, Section 3), see vignette("twinstim").

Usage

twinstim(endemic, epidemic, siaf, tiaf, qmatrix = data$qmatrix, data,
         subset, t0 = data$stgrid$start[1], T = tail(data$stgrid$stop,1),
         na.action = na.fail, start = NULL, partial = FALSE,
         epilink = "log", control.siaf = list(F = list(), Deriv = list()),
         optim.args = list(), finetune = FALSE,
         model = FALSE, cumCIF = FALSE, cumCIF.pb = interactive(),
         cores = 1, verbose = TRUE)

Arguments

endemic	right-hand side formula for the exponential (Cox-like multiplicative) endemic component. May contain offsets (to be marked by the special function offset). If omitted or ~0 there will be no endemic component in the model. A type-specific endemic intercept can be requested by including the term (1|type) in the formula.
epidemic	formula representing the epidemic model for the event-specific covariates (marks) determining infectivity. Offsets are not implemented here. If omitted or ~0 there will be no epidemic component in the model.
siaf	spatial interaction function. Possible specifications are: NULL or missing, corresponding to siaf.constant(), i.e. spatially homogeneous infectivity independent of the distance from the host a list as returned by siaf or, more commonly, generated by a predefined interaction function such as siaf.gaussian as in Meyer et al. (2012) or siaf.powerlaw as in Meyer and Held (2014). The latter requires unique event locations, possibly after random tie-breaking (untie) or imputation of interval-censored locations. siaf.exponential is a simpler alternative. a numeric vector corresponding to the knots of a step function, i.e. the same as siaf.step(knots) If you run into “false convergence” with a non-constant siaf specification, the numerical accuracy of the cubature methods is most likely too low (see the control.siaf argument).
tiaf	temporal interaction function. Possible specifications are: NULL or missing, corresponding to tiaf.constant(), i.e. time-constant infectivity a list as returned by tiaf or by a predefined interaction function such as tiaf.exponential a numeric vector corresponding to the knots of a step function, i.e. the same as tiaf.step(knots)
qmatrix	square indicator matrix (0/1 or FALSE/TRUE) for possible transmission between the event types. The matrix will be internally converted to logical. Defaults to the Q matrix specified in data.
data	an object of class "epidataCS".
subset	an optional vector evaluating to logical indicating a subset of data$events to keep. Missing values are taken as FALSE. The expression is evaluated in the context of the data$events@data data.frame, i.e. columns of this data.frame may be referenced directly by name.
t0, T	events having occurred during (-Inf;t0] are regarded as part of the prehistory H_0 of the process. Only events that occurred in the interval (t0; T] are considered in the likelihood. The time point t0 (T) must be an element of data$stgrid$start (data$stgrid$stop). The default time range covers the whole spatio-temporal grid of endemic covariates.
na.action	how to deal with missing values in data$events? Do not use na.pass. Missing values in the spatio-temporal grid data$stgrid are not accepted.
start	a named vector of initial values for (a subset of) the parameters. The names must conform to the conventions of twinstim to be assigned to the correct model terms. For instance, "h.(Intercept)" = endemic intercept, "h.I(start/365)" = coefficient of a linear time trend in the endemic component, "h.factorB" = coefficient of the level B of the factor variable factor in the endemic predictor, "e.(Intercept)" = epidemic intercept, "e.VAR" = coefficient of the epidemic term VAR, "e.siaf.2" = second siaf parameter, "e.tiaf.1" = first tiaf parameter. Elements which don't match any of the model parameters are ignored. Alternatively, start may also be a named list with elements "endemic" or "h", "epidemic" or "e", "siaf" or "e.siaf", and "tiaf" or "e.tiaf", each of which containing a named numeric vector with the term labels as names (i.e. without the prefix "h.", "e.", etc). Thus, start=list(endemic=c("(Intercept)"=-10)) is equivalent to start=c("h.(Intercept)"=-10).
partial	logical indicating if a partial likelihood similar to the approach by Diggle et al. (2010) should be used (default is FALSE). Note that the partial likelihood implementation is not well tested.
epilink	a character string determining the link function to be used for the epidemic linear predictor of event marks. By default, the log-link is used. The experimental alternative epilink = "identity" (for use by epitest) does not guarantee the force of infection to be positive. If this leads to a negative total intensity (endemic + epidemic), the point process is not well defined (the log-likelihood will be NaN).
control.siaf	a list with elements "F" and "Deriv", which are lists of extra arguments passed to the functions siaf$F and siaf$Deriv, respectively. These arguments control the accuracy of the cubature routines from package polyCub involved in non-constant siaf specifications, e.g., the bandwidth of the midpoint rule polyCub.midpoint, the number of Gaussian quadrature points for polyCub.SV, or the relative tolerance of integrate in polyCub.iso. For instance, siaf.gaussian(F.adaptive = TRUE) uses the midpoint-cubature polyCub.midpoint with an adaptive bandwidth of eps=adapt*sd to numerically integrate the kernel f(\bold{s}), and the default adapt value (0.1) can be overwritten by setting control.siaf$F$adapt. However, the default version siaf.gaussian() as well as siaf.powerlaw() and friends use polyCub.iso and thus accept control arguments for the standard integrate routine (such as rel.tol) via control.siaf$F and control.siaf$Deriv. This argument list is ignored in the case siaf=siaf.constant() (which is the default if siaf is unspecified).
optim.args	an argument list passed to optim, or NULL, in which case no optimization will be performed but the necessary functions will be returned in a list (similar to what is returned if model = TRUE). Initial values for the parameters may be given as list element par in the order (endemic, epidemic, siaf, tiaf). If no initial values are provided, crude estimates will be used for the endemic intercept and the Gaussian kernel, -9 for the epidemic intercept, and zeroes for the remaining parameters. Any initial values given in the start argument take precedence over those in par. Note that optim receives the negative log-likelihood for minimization (thus, if used, optim.args$control$fnscale should be positive). The hessian argument defaults to TRUE, and in the control list, traceing is enabled with REPORT=1 by default. By setting optim.args$control$trace = 0, all output from the optimization routine is suppressed. For the partial likelihood, the analytic score function and the Fisher information are not implemented and the default is to use robust method="Nelder-Mead" optimization. There may be an extra component fixed in the optim.args list, which determines which parameters should stick to their initial values. This can be specified by a logical vector of the same length as the par component, by an integer vector indexing par or by a character vector following the twinstim naming conventions. Furthermore, if isTRUE(fixed), then all parameters are fixed at their initial values and no optimization is performed. Importantly, the method argument in the optim.args list may also be "nlminb", in which case the nlminb optimizer is used. This is also the default for full likelihood inference. In this case, not only the score function but also the expected Fisher information can be used during optimization (as estimated by what Martinussen and Scheike (2006, p. 64) call the “optional variation process”, or see Rathbun (1996, equation (4.7))). In our experience this gives better convergence than optim's methods. For method="nlminb", the following parameters of the optim.args$control list may be named like for optim and are renamed appropriately: maxit (-> iter.max), REPORT (-> trace, default: 1), abstol (-> abs.tol), and reltol (-> rel.tol, default: 1e-6). For nlminb, a logical hessian argument (default: TRUE) indicates if the negative expected Fisher information matrix should be used as the Hessian during optimization (otherwise a numerical approximation is used). Similarly, method="nlm" should also work but is not recommended here.
finetune	logical indicating if a second maximisation should be performed with robust Nelder-Mead optim using the resulting parameters from the first maximisation as starting point. This argument is only considered if partial = FALSE and the default is to not conduct a second maximization (in most cases this does not improve upon the MLE).
model	logical indicating if the model environment should be kept with the result, which is required for intensityplots and R0(..., trimmed = FALSE). Specifically, if model=TRUE, the return value will have the evaluation environment set as its environment, and the returned functions element will contain the log-likelihood function (or partial log-likelihood function, if partial = TRUE), and optionally the score and the expected Fisher information functions (not for the partial likelihood, and only if siaf and tiaf provide the necessary derivatives). Note that fitted objects with a model environment might consume quite a lot of memory since they contain the data.
cumCIF	logical (default: FALSE) indicating whether to calculate the fitted cumulative ground intensity at event times. This is the residual process, see residuals.twinstim.
cumCIF.pb	logical indicating if a progress bar should be shown during the calculation of cumCIF. Defaults to do so in an interactive R session, and will be FALSE if cores != 1.
cores	number of processes to use in parallel operation. By default twinstim runs in single-CPU mode. Currently, only the multicore-type of parallel computing via forking is supported, which is not available on Windows, see mclapply in package parallel. Note that for a memoised siaf.step kernel, cores=1 is fixed internally since parallelization would slow down model fitting significantly.
verbose	logical indicating if information should be printed during execution. Defaults to TRUE.

Details

The function performs maximum likelihood inference for the additive-multiplicative spatio-temporal intensity model described in Meyer et al. (2012). It uses nlminb as the default optimizer and returns an object of class "twinstim". Such objects have print, plot and summary methods. The summary output can be converted via corresponding xtable or toLatex methods. Furthermore, the usual accessor methods are implemented, including coef, vcov, logLik, residuals, and update. Additional functionality is provided by the R0 and simulate methods.

Value

Returns an S3 object of class "twinstim", which is a list with the following components:

coefficients	vector containing the MLE.
loglik	value of the log-likelihood function at the MLE with a logical attribute "partial" indicating if the partial likelihood was used.
counts	number of log-likelihood and score evaluations during optimization.
converged	either TRUE (if the optimizer converged) or a character string containing a failure message.
fisherinfo	expected Fisher information evaluated at the MLE. Only non-NULL for full likelihood inference (partial = FALSE) and if spatial and temporal interaction functions are provided with their derivatives.
fisherinfo.observed	observed Fisher information matrix evaluated at the value of the MLE. Obtained as the negative Hessian. Only non-NULL if optim.args$method is not "nlminb" and if it was requested by setting hessian=TRUE in optim.args.
fitted	fitted values of the conditional intensity function at the events.
fittedComponents	two-column matrix with columns "h" and "e" containing the fitted values of the endemic and epidemic components, respectively. (Note that rowSums(fittedComponents) == fitted.)
tau	fitted cumulative ground intensities at the event times. Only non-NULL if cumCIF = TRUE. This is the “residual process” of the model, see residuals.twinstim.
R0	estimated basic reproduction number for each event. This equals the spatio-temporal integral of the epidemic intensity over the observation domain (t0;T] x W for each event.
npars	vector describing the lengths of the 5 parameter subvectors: endemic intercept(s) \beta_0(\kappa), endemic coefficients \beta, epidemic coefficients \gamma, parameters of the siaf kernel, and parameters of the tiaf kernel.
qmatrix	the qmatrix associated with the epidemic data as supplied in the model call.
bbox	the bounding box of data$W.
timeRange	the time range used for fitting: c(t0,T).
formula	a list containing the four main parts of the model specification: endemic, epidemic, siaf, and tiaf.
xlevels	a record of the levels of the factors used in fitting.
control.siaf	see the “Arguments” section above.
optim.args	input optimizer arguments used to determine the MLE.
functions	if model=TRUE this is a list with components ll, sc and fi, which are functions evaluating the log-likelihood, the score function and the expected Fisher information for a parameter vector \theta. The environment of these function is the model environment, which is thus retained in the workspace if model=TRUE. Otherwise, the functions component is NULL.
call	the matched call.
runtime	the proc.time-queried time taken to fit the model, i.e., a named numeric vector of length 5 of class "proc_time", with the number of cores set as additional attribute.

If model=TRUE, the model evaluation environment is assigned to this list and can thus be queried by calling environment() on the result.

Note

twinstim makes use of the memoise package if it is available – and that is highly recommended for non-constant siaf specifications to speed up calculations. Specifically, the necessary numerical integrations of the spatial interaction function will be cached such that they are only calculated once for every state of the siaf parameters during optimization.

Author(s)

Sebastian Meyer

Contributions to this documentation by Michael Höhle and Mayeul Kauffmann.

References

Diggle, P. J., Kaimi, I. & Abellana, R. (2010): Partial-likelihood analysis of spatio-temporal point-process data. Biometrics, 66, 347-354.

Martinussen, T. and Scheike, T. H. (2006): Dynamic Regression Models for Survival Data. Springer.

Meyer, S. (2010): Spatio-Temporal Infectious Disease Epidemiology based on Point Processes. Master's Thesis, Ludwig-Maximilians-Universität München.
Available as https://epub.ub.uni-muenchen.de/11703/

Meyer, S., Elias, J. and Höhle, M. (2012): A space-time conditional intensity model for invasive meningococcal disease occurrence. Biometrics, 68, 607-616. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1111/j.1541-0420.2011.01684.x")}

Meyer, S. and Held, L. (2014): Power-law models for infectious disease spread. The Annals of Applied Statistics, 8 (3), 1612-1639. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1214/14-AOAS743")}

Meyer, S., Held, L. and Höhle, M. (2017): Spatio-temporal analysis of epidemic phenomena using the R package surveillance. Journal of Statistical Software, 77 (11), 1-55. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.18637/jss.v077.i11")}

Rathbun, S. L. (1996): Asymptotic properties of the maximum likelihood estimator for spatio-temporal point processes. Journal of Statistical Planning and Inference, 51, 55-74.

See Also

vignette("twinstim").

There is a simulate.twinstim method, which simulates the point process based on the fitted twinstim.

A discrete-space alternative is offered by the twinSIR modelling framework.

Examples

# Load invasive meningococcal disease data
data("imdepi")


### first, fit a simple endemic-only model

m_noepi <- twinstim(
    endemic = addSeason2formula(~ offset(log(popdensity)) + I(start/365-3.5),
                                S=1, period=365, timevar="start"),
    data = imdepi, subset = !is.na(agegrp)
)

## look at the model summary
summary(m_noepi)

## there is no evidence for a type-dependent endemic intercept (LR test)
m_noepi_type <- update(m_noepi, endemic = ~(1|type) + .)
pchisq(2*c(logLik(m_noepi_type)-logLik(m_noepi)), df=1, lower.tail=FALSE)


### add an epidemic component with just the intercept, i.e.
### assuming uniform dispersal in time and space up to a distance of
### eps.s = 200 km and eps.t = 30 days (see summary(imdepi))

m0 <- update(m_noepi, epidemic=~1, model=TRUE)

## summarize the model fit
summary(m0, correlation = TRUE, symbolic.cor = TRUE)

## the default confint-method can be used for Wald-CI's
confint(m0, level=0.95)

## same "untrimmed" R0 for every event (simple epidemic intercept model)
summary(R0(m0, trimmed=FALSE))

## plot the path of the fitted total intensity
plot(m0, "total intensity", tgrid=500)

if (surveillance.options("allExamples")) {
## extract "residual process" integrating over space (takes some seconds)
res <- residuals(m0)
# if the model describes the true CIF well _in the temporal dimension_,
# then this residual process should behave like a stationary Poisson
# process with intensity 1
plot(res, type="l"); abline(h=c(0, length(res)), lty=2)
# easier, with CI and serial correlation:
checkResidualProcess(m0)
}

## Not run: 
  ## NB: in contrast to nlminb(), optim's BFGS would miss the
  ##     likelihood maximum wrt the epidemic intercept
  m0_BFGS <- update(m_noepi, epidemic=~1, optim.args = list(method="BFGS"))
  format(cbind(nlminb=coef(m0), BFGS=coef(m0_BFGS)), digits=3, scientific=FALSE)
  m0_BFGS$fisherinfo   # singular Fisher information matrix here
  m0$fisherinfo
  logLik(m0_BFGS)
  logLik(m0)
  ## nlminb is more powerful since we make use of the analytical fisherinfo
  ## as estimated by the model during optimization, which optim cannot

## End(Not run)


### an epidemic-only model?
## for a purely epidemic model, all events must have potential source events
## (otherwise the intensity at the observed event would be 0)

## let's focus on the C-type for this example
imdepiC <- subset(imdepi, type == "C")
table(summary(imdepiC)$nSources)
## 106 events have no prior, close events (in terms of eps.s and eps.t)
try(twinstim(epidemic = ~1, data = imdepiC))  # detects this problem
## let's assume spatially unbounded interaction
imdepiC_infeps <- update(imdepiC, eps.s = Inf)
(s <- summary(imdepiC_infeps))
table(s$nSources)
## for 11 events, there is no prior event within eps.t = 30 days
## (which is certainly true for the first event)
plot(s$counter, main = "Number of infectious individuals over time (eps.t = 30)")
rug(imdepiC_infeps$events$time)
rug(imdepiC_infeps$events$time[s$nSources == 0], col = 2, lwd = 3)
## An endemic component would catch such events (from unobserved sources),
## otherwise a longer infectious period would need to be assumed and
## for the first event to happen, a prehistory is required (e.g., t0 = 31).
## As an example, we fit the data only until T = 638 (all events have ancestors)
m_epi <- twinstim(epidemic = ~1, data = imdepiC_infeps, t0 = 31, T = 638)
summary(m_epi)


if (surveillance.options("allExamples")) withAutoprint({

### full model with interaction functions (time-consuming)
## estimate an exponential temporal decay of infectivity
m1_tiaf <- update(m0, tiaf=tiaf.exponential())
plot(m1_tiaf, "tiaf", scaled=FALSE)

## estimate a step function for spatial interaction
summary(sourceDists <- getSourceDists(imdepi, "space"))
(knots <- quantile(sourceDists, c(5,10,20,40)/100))
m1_fstep <- update(m0, siaf=knots)
plot(m1_fstep, "siaf", scaled=FALSE)
rug(sourceDists, ticksize=0.02)

## estimate a continuously decreasing spatial interaction function,
## here we use the kernel of an isotropic bivariate Gaussian
m1 <- update(m0, siaf = siaf.gaussian())
AIC(m_noepi, m0, m1_fstep, m1)
summary(m1)  # e.siaf.1 is log(sigma), no test for H0: log(sigma) = 0
exp(confint(m1, "e.siaf.1"))  # a confidence interval for sigma
plot(m1, "siaf", scaled=FALSE)
## alternative: siaf.powerlaw() with eps.s=Inf and untie()d data,
##              see vignette("twinstim")

## add epidemic covariates
m2 <- update(m1, epidemic = ~ 1 + type + agegrp)
AIC(m1, m2)   # further improvement
summary(m2)
  
## look at estimated R0 values by event type
tapply(R0(m2), imdepi$events@data[names(R0(m2)), "type"], summary)

})

surveillance documentation built on Oct. 2, 2024, 1:08 a.m.